APPLICATION: FLOOD FORECASTING MODEL FOR KLODZKO CATCHMENT (POLAND) - Modelling Uncertainty in Flood Forecasting Systems - page 109

5.5 Conclusions and discussion
This chapter presented a methodology for the treatment of precipitation uncertainty in a
rainfall-runoff-routing model in the framework of fuzzy set theory (using the Extension
Principle) assisted by genetic algorithms. The methodology uses a reconstructed
precipitation time series based on the disaggregation of precipitation into subperiods.
This methodology is particularly useful in the absence of a probabilistic quantitative
precipitation forecast. The methodology is independent of the structure of the forecasting
model. In other words, it can be used with any rainfall-runoff-routing type deterministic
model.
The results show the good potential of the fuzzy Extension Principle combined with a
genetic algorithm for the propagation of uncertainty. The results also show that the
output uncertainty due to the uncertainty in the temporal and spatial distributions can be
significantly dominant over the uncertainty due to the uncertainty in the magnitude of the
precipitation. This suggests that using space- and time-averaged precipitation over the
catchment may lead to erroneous forecasts. The estimated uncertainty in the output may
seem small compared to the magnitude of the flood. This is due to the relatively short
forecast period (3 hours). Obviously increasing the forecast period significantly increases
the uncertainty in the forecasted precipitation and thereby increases the output
uncertainty. Moreover, it is to be noted that the estimated uncertainty is only due to the
uncertainty in the precipitation. It does not include the parameter and model uncertainty.
An attempt has also been made to answer the question concerning an appropriate
number of subperiods. Whereas the estimated uncertainty with reconstructed and
uniform precipitation differed significantly (Fig. 5.12), the results with 3 and 6
subperiods showed only a small difference (Fig. 5.16). Therefore, in this particular
example 3 subperiods seem good enough. In general, the determination of the number of
subperiods should be governed by the consideration that: (i) the uncertainty should not
be underestimated or overestimated beyond a reasonable limit, (ii) the computational
requirements should not be too large, (iii) the subperiods should be large enough for the
disaggregated precipitations to remain realistic, and (iv) the length of the subperiod
should be of the same order of magnitude as the correlation time of the precipitation
signal.